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Found 4 papers, 0 papers with code
Single Domain Generalization via Normalised Cross-correlation Based Convolutions
This approach is invariant to both affine shifts and changes in energy within a local feature patch and eliminates the need for commonly used non-linear activation functions.
ITSA: An Information-Theoretic Approach to Automatic Shortcut Avoidance and Domain Generalization in Stereo Matching Networks
State-of-the-art stereo matching networks trained only on synthetic data often fail to generalize to more challenging real data domains.
Achieving Domain Robustness in Stereo Matching Networks by Removing Shortcut Learning
We provide evidence that demonstrates that learning of features in the synthetic domain by a stereo matching network is heavily influenced by two "shortcuts" presented in the synthetic data: (1) identical local statistics (RGB colour features) between matching pixels in the synthetic stereo images and (2) lack of realism in synthetic textures on 3D objects simulated in game engines.
Adjusting Bias in Long Range Stereo Matching: A semantics guided approach
Consequently, the learning algorithms often produce unreliable depth estimates of foreground objects, particularly at large distances~($>50$m).
